@kleosr/pe2-cli/src/templates.js

🚀 KleoSr PE2-CLI: Convert raw prompts to PE2-optimized prompts using adaptive intelligence

export const DIFFICULTY_INDICATORS = {
  NOVICE: '🟢',
  INTERMEDIATE: '🟡',
  ADVANCED: '🟠',
  EXPERT: '🔴',
  MASTER: '🟣'
};

export const getInitialTemplate = (rawPrompt) => `
You are an expert prompt engineer specializing in PE² (Prompt Engineering 2.0) optimization.

Transform the following raw prompt into a structured PE² format with exactly these 5 sections:

Raw prompt to optimize:
---

${rawPrompt}
---


Return ONLY a valid JSON object with these exact keys:
{
  "context": "Comprehensive problem description and scope",
  "role": "Expert persona for the LLM to adopt",
  "task": "Step-by-step breakdown of required actions",
  "constraints": "Rules, limitations, and boundaries",
  "output": "Expected format and structure of the response"
}

CRITICAL: Return ONLY the JSON object. No explanations or additional text.
`;

export const getRefinementTemplate = (currentPromptJson, iterationNum) => `
You are refining a PE² prompt for iteration ${iterationNum}.

Current PE² Prompt:
${currentPromptJson}

Analyze and improve the prompt by:
1. Enhancing clarity and specificity
2. Ensuring all PE² sections are well-balanced
3. Optimizing for better LLM performance
4. Maintaining logical consistency
5. Improving actionability

Return ONLY the improved JSON object with the same 5 sections.
`;

export function formatMarkdownOutput(pe2Prompt, history, metrics, difficulty, complexityScore) {
  const indicator = DIFFICULTY_INDICATORS[difficulty];
  return `
# PE²-Optimized Prompt ${indicator}

**Difficulty Level:** ${difficulty} | **Complexity Score:** ${complexityScore}/20 | **Generated:** ${new Date().toLocaleString()}

## Agentic Analysis
- **Optimization Level:** ${metrics.optimization_level || 'N/A'}
- **Overall Quality:** ${metrics.quality_score || 'N/A'}

## Context
${pe2Prompt.context || 'N/A'}

## Role
${pe2Prompt.role || 'N/A'}

## Task
${pe2Prompt.task || 'N/A'}

## Constraints
${pe2Prompt.constraints || 'N/A'}

## Output
${pe2Prompt.output || 'N/A'}

---


# Refinement History
${history.map(item => `### Iteration ${item.iteration}\n- ${item.edits}\n`).join('\n')}

---


# Performance Metrics
- **Estimated Accuracy Gain**: ${metrics.accuracy_gain || 'N/A'}
- **Complexity Analysis**: ${difficulty} level prompt with ${complexityScore}/20 complexity score
- **Optimization Level**: ${history.length} iterations applied
- **Generated by**: KleoSr PE²-CLI

---


*Generated with ❤️ by KleoSr PE²-CLI*
`;
}